Nosocomial hospital infections are a significant public health concern as they severely harm patients, disrupt normal operations, and increase hospital costs. To reduce these risks and allow for preemptive responses, there is a critical need for routine surveillance of nosocomial pathogens within a hospital environment (patients, staff, ventilation equipment). With the decreasing costs of sequencing, whole genome sequencing is increasingly becoming viable as a diagnostic tool. However, translating its genomic information into a distinctive surveillance signature has been a significant challenge. Indeed a one-year hospital study of A. baumannii showed that strains continuously evolved during that period through complex mixing from multiple founder strains. As such, current core-alignment tools will be unable to separate clonal re-circulating strains that differ in their Pan Genome content. Moreover, most comparative alignment tools rarely perform resistome analysis, which has been employed in multiple research studies to separate outbreak strains from clonal strains. Our Phase 1 proposal will use E. coli as a test case to build the first integrated strain-resistome framework for routine surveillance of nosocomial pathogens. Our approach will employ a unique Pan Genome that will translate the entire genome content of the unknown strain into a distinctive strain signature. Likewise, our resistome analysis will employ a Pan Resistome that will translate the resistome content of the unknown strain into a distinctive resistome signature. Taken together, these strain and resistome signatures will provide a comprehensive set of genome-markers that will be used to connect newly sequenced strains in the event of a new infection. To confirm our approach, we tested over 342 E. coli strains from a single hospital. Our preliminary studies show that we are able to separate clusters from other clonal strains within the same clade. This separation was done independently using both strain and resistome comparisons. Our Phase 1 aims are: Aim1--Develop a screening framework that rapidly identifies unknown strains and assigns them to a node; Aim2--Develop a strain analysis framework that analyzes the unknown strain and establishes a list of closely-related strain clusters; Aim3--Develop a resistome analysis framework that analyzes the unknown strain and establishes a list of closely-related resistomes. Our cloud framework will be built completely in AWS marketplace using commercial components. It will be evaluated against the complete set of E. coli data from the FDA GenomeTrakr project.